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Australian Bureau of Statistics (ABS)

by seansoreilly

query_dataset

Retrieve specific data from Australian Bureau of Statistics (ABS) datasets using customizable filters to meet analytical needs.

Instructions

Query a specific ABS dataset with optional filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetIdYesID of the dataset to query (e.g., C21_G01_LGA)

Implementation Reference

  • The main handler for tool calls, which specifically implements the logic for 'query_dataset' by validating input, constructing the ABS API URL, fetching the data with axios, and returning the JSON response.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      try {
        const { name, arguments: args } = request.params;
    
        if (name !== "query_dataset") {
          throw new Error(`Unknown tool: ${name}`);
        }
    
        if (!args?.datasetId || typeof args.datasetId !== "string") {
          throw new Error("datasetId is required and must be a string");
        }
    
        const url = `${ABS_API_BASE}/data/${args.datasetId}/all?format=json&dimensionAtObservation=AllDimensions`;
        
        try {
          const response = await axios.get(url);
          return {
            content: [{
              type: "text",
              text: JSON.stringify(response.data, null, 2)
            }]
          };
        } catch (error) {
          if (error instanceof AxiosError && error.response) {
            throw new Error(`ABS API Error: ${error.response.status} - ${JSON.stringify(error.response.data)}`);
          }
          throw error;
        }
      } catch (error) {
        const errorMessage = error instanceof Error ? error.message : String(error);
        throw new Error(`Error querying dataset: ${errorMessage}`);
      }
    });
  • src/index.ts:29-48 (registration)
    Registers the 'query_dataset' tool by defining its name, description, and input schema in the list tools response.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: [
          {
            name: "query_dataset",
            description: "Query a specific ABS dataset with optional filters",
            inputSchema: {
              type: "object",
              required: ["datasetId"],
              properties: {
                datasetId: {
                  type: "string",
                  description: "ID of the dataset to query (e.g., C21_G01_LGA)"
                }
              }
            }
          }
        ]
      };
    });
  • Defines the input schema for the query_dataset tool, requiring a 'datasetId' string.
    inputSchema: {
      type: "object",
      required: ["datasetId"],
      properties: {
        datasetId: {
          type: "string",
          description: "ID of the dataset to query (e.g., C21_G01_LGA)"
        }
      }
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'query' but doesn't clarify if this is a read-only operation, what permissions are needed, potential rate limits, or the response format. The mention of 'optional filters' adds some context, but critical behavioral traits like safety, performance, or output characteristics are missing, leaving significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the core action and key feature (optional filters). It's front-loaded with the main purpose and avoids unnecessary words. However, it could be slightly more informative without losing conciseness, such as hinting at the query's output or data scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a query operation with potential filters), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the query returns (e.g., raw data, aggregated results), how filters are applied, or any constraints. For a tool that likely involves data retrieval and processing, more context is needed to ensure proper usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal value beyond the input schema, which has 100% coverage for the single parameter 'datasetId'. It implies filtering is possible but doesn't detail how filters work or their syntax. With high schema coverage, the baseline is 3, as the schema already documents the parameter adequately, and the description doesn't significantly enhance understanding of parameter usage or semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('query') and target ('ABS dataset'), which provides a basic understanding of purpose. However, it's vague about what 'query' entails (e.g., retrieving data, statistics, or metadata) and doesn't specify resource scope beyond 'dataset'. With no siblings, differentiation isn't needed, but the purpose lacks specificity beyond general terms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions 'optional filters' as a usage hint, implying it can be used with or without filtering. However, it provides no guidance on when to use this tool (e.g., for data analysis vs. other operations) or any prerequisites. With no sibling tools, alternatives aren't relevant, but the lack of contextual guidance results in minimal assistance for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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